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\begin{document}

\title{TagLeak: Non-intrusive and Battery-free Liquid Leakage Detection with Backscattered Signals}

\author{
	\IEEEauthorblockN{Junchen Guo\IEEEauthorrefmark{1}, Ting Wang\IEEEauthorrefmark{1}, Meng Jin\IEEEauthorrefmark{2}\IEEEauthorrefmark{1}, Songzhen Yang\IEEEauthorrefmark{1}, Chengkun Jiang\IEEEauthorrefmark{1}, Long Liu\IEEEauthorrefmark{1} and Yuan He\IEEEauthorrefmark{1}}
	
	\IEEEauthorblockA{
		\textit{\IEEEauthorrefmark{1}School of Software, Tsinghua University, P.R. China} \\
		\textit{\IEEEauthorrefmark{2}School of Information and Technology, Northwest University, P.R. China} \\
		gjc16@mails.tsinghua.edu.cn, wangting96@mails.tsinghua.edu.cn, mengj@stumail.nwu.edu.cn, yangsz14@mails.tsinghua.edu.cn, \\
		jck15@mails.tsinghua.edu.cn, liulong16@mails.tsinghua.edu.cn, heyuan@tsinghua.edu.cn
	}
}

\maketitle

\begin{abstract}

Leakage detection is a crucial issue for factories with numerous pipelines and valves. Conventional methods for leakage detection are mainly rely on manual checking, which results in both high delay and low accuracy. In this paper, we propose TagLeak, a real-time and low-cost system for automatic leakage detection with commercial off-the-shelf (COTS) RFID devices. The key intuition behind TagLeak is that the leaked liquid around tags will change the phase and RSS (Received Signal Strength) of the tags’ signal, which can be exploited as an indicator for liquid leakage. Multiple challenges need to be addressed before we can turn the idea into a functional system, including: i) it is difficult to detect the slight signal variation that caused by the leaked liquid, based on the coarse-grained RSS sequence; ii) multipath and interferences can undermine the tags’ signal, making the variation caused by leaked liquid more difficult to detect.

We propose solutions to all the challenges and evaluate the system’s performance in different environments. The experimental results tell that TagLeak achieves a higher than 90.2\% average accuracy while keeps the recall below 14.3\%. Moreover, as an exploration of the industrial Internet, we have deployed TagLeak in a real-world digital twin system Pavatar for liquid leakage detection in an ultra-high-voltage converter station (UHVCS).


\end{abstract}

\section{Introduction}

Liquid leakage is a common problem in today’s factories with numerous pipelines and valves. For example, in large-scale rotating machinery like electric generators and synchronous compensators, multiple auxiliary machines for water cooling, lubricant looping, and etc., suffer from potential liquid leakage problem at the unwelded flanges (which is termed as leakage point in this paper), as shown in Fig. \ref{fig:background}. Liquid leakage not only affects the normal operation of the machines, but also leads to serious industrial accidents, e.g. circuit shorts due to water leakage, or even fire disasters due to lubricant leakage. 

At the heart of addressing this scourge is timely detection. However, today’s liquid leakage detection methods still depend on manual checking, e.g., by visual inspections or by touching the surfaces of those machines. Either way can inevitably lead to high detection delay and low accuracy, meanwhile incurs additional labor cost. What’s worse, manual dependent methods cannot provide quantized or digitalized monitoring result, which imposes difficulties for further analysis.

There is an urgent need for an automatic leakage detection method. Exploiting water-leakage sensors seems an effective solution, but this method suffers high overhead. Specifically, today’s large-scale machinery usually involves massive leakage points,  which scattered everywhere of the factory (as shown in Fig. \ref{fig:background}). The detection range of the water-leakage sensors, however, is quite limited. Therefore, covering all the potential leakage points requires dens deployment of the sensors. Besides, liquid leakage occurs repeatedly and randomly, which requires frequent renewal of the disposable water-leakage sensors. Therefore, water-leakage sensors are seldom adopted in industry due to the unaffordable cost. Camera-based detection may become a low-cost alternative, but it fails in the absence of a line-of-sight to the objects or in dark environment.

\begin{figure}[tb]
	\centering
	\includegraphics[width=0.5\textwidth]{img/background2.pdf}
	\caption{\textbf{Liquid leakage problems in modern factories.}}
	\label{fig:background}
\end{figure}

In this paper, we turn our attentions to a mature technology, RFID (Radio Frequency IDentification). RFID is evolving as a major technology enabler for physical phenomenon sensing, such as vibration inspection \cite{TagBeat}, eccentricity detection \cite{RED}, humidity sensing\cite{Hum}, package verification \cite{Package}, activity recognition \cite{Shop}, touch detection \cite{RIO} and material identification \cite{TagScan}. 
%
The reason for such widespread use of RFID-based sensing are: i) it enables very low lost sensing at high volumes; and ii) it functions in NLOS scenarios and dark environments. This motivates us to think about a challenging question: can RFID-based sensing detect liquid leakage? The answer is yes.

Many works have shown that liquid will change the phase and RSS (Received Signal Strength) of the RF signal. This can be exploit as an indicator for liquid leakage. Though the basic idea sounds straightforward, it is non-trivial to realize this goal due to the following challenges:

\begin{itemize}
	
\item \textbf{Challenge 1}: The resolution of RSS is extremely low. Consider that the signal variation that caused by the leaked liquid is much weaker than the line-of-sight (LOS) signal at the receiver side, so it is challenging to detect such a slight signal variation based on the coarse-grained RSS sequence;

\item \textbf{Challenge 2}: Signal measurement suffers from multiple sources of interferences, such as the vibration of the machine, people’s movements around the tags or reader, the rich multipaths caused by complicated pipelines, etc. These interferences undermine the tags’ signal, making the variation caused by leaked liquid more difficult to detect.

\end{itemize}

\begin{figure}[tb]
	\centering
	\includegraphics[width=0.43\textwidth]{img/preliminary.pdf}
	\caption{\textbf{Basic experiment setup of TagLeak.}}
	\label{fig:preliminary}
\end{figure}

In this paper, we propose TagLeak, a real-time and low-cost system for automatic leakage detection with commercial oﬀ-the-shelf (COTS) RFID devices. TagLeak involves a dual-tag leakage detection device termed as Leakage Detection Tag (LDT), and a Hidden Markov Model (HMM) based leakage detection algorithm. Specifically, LDT leverage the {\em coupling effect} between two adjacent RFID tags to filter out the noisy signal that introduced by the interferences and enlarge the signal variation that caused by the liquid leakage. Then the HMM-based detection algorithm is designed to continuously track the variation of the signals from the two tags, which can detect liquid leak with high sensitivity and accuracy.

The contributions of this paper are summarized as follows:

\begin{itemize}
	\item TagLeak is the first RFID-based system that detect the liquid leakage with high accuracy, using only the coarse-grained backscatter signal. It solves a practical problem for factories with numerous pipelines and valves, which require liquid detection methods that is highly accurate, cost-effective, and robust in different scenarios.
	
	\item We design a dual-tag leakage detection device which makes TagLeak immune to many negative impacts using the coupling effect between two adjacent tags, and a HMM based method to detect liquid leakage in real-time.
	
	\item We implement a prototype of TagLeak and evaluate it across various scenarios. On average, TagLeak achieves a higher than 90.2\% accuracy while keeps the recall below 14.3\%. As an exploration of the industrial Internet, we have deployed TagLeak in a real-world digital twin system Pavatar \cite{Pavatar}\cite{Pavatar-paper} for liquid leakage detection in an ultra-high voltage converter station. 
\end{itemize}

The rest of this paper is organized as follows.
%
We introduces some related works of TagLeak in Section II.
%
The preliminary intuition of TagLeak is discussed in Section III.
%
The overview and design details of TagLeak are presented in Section IV.
%
Section V describes the implementation and evaluation of TagLeak.
%
Finally, we conclude TagLeak and discuss future works in Section VI.

\section{Related Works}
\subsection{Traditional leakage detection methods}
Most commodity leakage detection methods exploit water-leakage sensors \cite{LeakFilm}, which detect leakage based on the conductivity of the liquid. These sensors consist of a pair of electrodes and an insulator that separates them. Liquid leakage will cause a short circuit in the two electrodes and then the impedance between them drops sharply. This triggers an alarm for liquid leakage. Although this method achieves high detection accuracy, the wired and intrusive design and the limited sensing area greatly hampers the application in the scenarios with complicated pipelines. The advance in wireless sensor networks provides a more convenient solution \cite{GreenOrbs}\cite{CitySee}, but the high-cost of the sensor nodes impedes them from dens deployment. Compared with the above method, RFID-based system has the advantages of non-intrusive, low-cost, and easy to deploy in various environment.

\subsection{RFID sensing applications}

Nowadays, RFID is evolving as a major candidate for cross-modal sensing in industrial scenarios. Specifically, the physical state of the tagged objects as well as their surroundings can be sensed by analyzing the backscatter radio-frequency (RF) signals from passive RFID tags.
%
For example, Tagoram \cite{Tagoram} and OmniTrack \cite{OmniTrack} are able to localize and tracking moving objects with high precision, just using COTS RFID tags and readers.
%
TagBeat \cite{TagBeat} and RED \cite{RED} are able to detect vibration frequency and eccentricity of a rotating machinery. Both of the above approaches are achieved by only inspecting the tiny variations of the RFID signal that caused by the target events (i.e., vibration and eccentricity).
%
RIO \cite{RIO} leverages the fact that the human touch will change the input impedance of a tag's antenna to detect touch actions on critical devices and track them.
%
Similarly, TagLeak discloses special signal patterns for cross-modal sensing, but focuses on another critical task of detecting the liquid leakage.


\subsection{Liquid sensing with RFID}

Based on the fact that, as a dielectric, the liquid material can significantly affect the propagation of RF signals by absorbing the electromagnetic fields \cite{Material1}\cite{Material2}, some works have applied RFID technologies to liquid sensing. 
%
For example, Rahul et al., utilize pure signal patterns caused by the liquid's absorption effect to roughly estimate the liquid volume in a beverage bottle \cite{Bottle}.
%
TagScan \cite{TagScan} models the signal attenuation when it travels through different kinds of liquid and proposes a distinct feature for material classification.
%
A few works sense ambient humidity with backscattered signals by either exposing the antenna circuit to the vapor \cite{Hum} or designing specific tags \cite{Humidity-add}\cite{Humidity-print}.
%
However, all of the above works cannot directly used in liquid leakage detection, because they either omit potential interferences or need complicated modifications of RFID systems.

Different from all the above works, TagLeak leverages the coupling effect for detecting the liquid leakage without manipulating the original COTS RFID systems, and is the first RFID-based leakage-detection system applied into practical industrial scenarios to our knowledge.

\begin{figure}[tb]
	\centering
	\includegraphics[width=0.5\textwidth]{img/interference.pdf}
	\caption{\textbf{Similar signal variation patterns of the two tags.}}
	\label{fig:interference}
\end{figure}

\section{Leakage Detection With LDT}

This section aims to show through both theoretical analysis and experiment that the leaked liquid will change the phase and RSS of the RFID signal, which can be exploited as an indicator for leakage detection.

Fig. \ref{fig:preliminary} shows the sketch of our leakage detection system, which involves a pair of Alien Ultra-High-Frequency (UHF) passive RFID tags (the reason for exploiting two tags is discussed in Section III-B) and a piece of absorbent cotton. The cotton is clamped between the two tags. We term such device as LDT (Leakage Detection Tag). The LDTs are fixed to the flanges. An ImpinJ Speedway R420 RFID reader and a Laird circular polarized antenna are deployed to provide continuous waves and receive the backscattered signals from the tags. When the liquid (e.g. water and lubricant), drops from the unwelded flange, the cotton absorbs it and makes a change to the RSSI and phase readings of both the two tags. The principle behind is discussed in the following of this section.

\subsection{Absorption Effect of Liquid}

Many works have shown that when the backscatter signal penetrates through the medium, different materials of the medium will cause different amounts of RSSI and phase variation of the signal. The amount of variation is mainly determined by the permittivity of the material \cite{Material1}\cite{Material2}. In the case of liquid leakage, the leaked liquid, which has higher permittivity than the air, will decrease the strength of the electric field near the tag. This further changes the impedance of the tag’s antenna. According to the theoretical transmission model of the RFID signal, the mismatch of the antenna impedance will not only affect the power charging procedure, but also degrades the signal reflected by the tag, which finally changes the RSSI and the phase of the backscatter signal \cite{RFID-principle}. Moreover, the change in permittivity can also change the wavelength of the backscatter signals, which further incurs different energy loss and phase change along the propagation paths. In summary, liquid leakage will change the signal of the tags, which acts as an indicator of liquid leakage.



\subsection{Handling Interference With Dual Tags}

Although the RSSI and phase readings provides promising information for leakage detection, we still face a challenge that the signal measurement of the tag suffers from multiple sources of interferences. The main interference comes from the vibration of the machine. Specifically, the tag attached to the flange will vibrates with the rotating machine, which can significantly affect the reading of the backscatter signals. Moreover, other dynamic interference like people’s movements around the tags or reader will also seriously change the backscatter signal.

% To deal with these interferences, we design the tag-stacked structure as shown in Fig. . Because the adjacent tags are expected to face similar interferences in various environment, the correlation of the two-stream data can be leveraged for interference localization and cancellation. Moreover, since the cotton is at the non-line-of-sight (NLOS) path of $T_2$, its readings are expected to present different features from those of $T_1$.

Thus, a reliable signal input should first generate a pattern with sufficient discrimination from the vibration interference in case of the liquid leakage, and be quickly distinguished from the other dynamic interference.

Fortunately, we find that exploiting the signal from two adjacent tags can help to eliminate the interferences. Specifically, the adjacent tags (as shown in Fig. \ref{fig:preliminary}) are expected to face similar interferences in various environments, thus the signal variation caused by the dynamic interference will be similar for both the two tags. Fig. \ref{fig:interference} shows the dynamic interferences induced by metal object, human body and paper box trigger similar signal variation patterns of the two tags.
%
Since the two tags are attached to the same vibrating target, the micro noise show similar patterns. We filter out the noise of a LDT attached to a centrifugal with a low-pass filter, and the results shown in Fig. \ref{fig:interference} suggest that the dynamic interference dominates the signal variation in spite of the irregular noise caused by the vibration.

Next, we will explain the signal variation pattern of the two tags induced by the liquid leakage is also much stronger than the vibration interference.

%Therefore, the gap between the RSSI and phase of the signal from the two tags is solely determined by the medium between these two tags. That is to say, observing the variation of the gap between the two signals can tell whether liquid leakage occurs.

\begin{figure}[tb]
	\centering
	\includegraphics[width=0.5\textwidth]{img/water_procedure_new.pdf}
	\caption{\textbf{Signal variations during the process of liquid leakage.}}
	\label{fig:water_procedure}
\end{figure}

\subsection{Near-Field Signal Model of LDT}
\label{sec:model}

In this subsection, we qualitatively analyze the near-field signal model of LDT, and explain the reason why it can provide a robust signal pattern for liquid leakage detection. 

\textbf{Signal Pattern of LDT.} We start by introducing the experimental result for the signal pattern of our LDT. Fig. \ref{fig:water_procedure} shows the signal variation during the procedure of the liquid leakage. The characteristics of the signal patterns in each stage of this procedure can be concluded as follows:

\begin{itemize}
	\item Before the leakage: The signals of the two tags are relatively stable before the leakage. However, there exists a huge gap ($\approx$ 8 dB) between the RSSI readings of them, although these two tags are extremely close to each other.
	 
	\item During the leakage: First, the signal of Tag 2 gets stronger at the beginning of the liquid leakage but later turns down along with the increased volume of the leaked liquid. In the meanwhile, the signal of Tag 1 keeps decreasing during the leakage. 
	
	\item After the leakage: When the absorbent cotton becomes saturated, the variation of the two signals begins to level off. As shown in Fig. \ref{fig:water_procedure} , the gap between the RSSI readings almost vanishes during this stage.
\end{itemize}

\begin{figure}[tb]
	\centering
	\includegraphics[width=0.5\textwidth]{img/model.pdf}
	\caption{\textbf{Near-field signal model of the LDT.}}
	\label{fig:tag_model}
\end{figure}

Therefore, we can easily recognize the signal patterns of the LDT in the above 3 stages by analyzing the signal variation trends of the two tags and the gap between their RSSI readings. The reason behind this phenomenon is explained as follows.

\textbf{Qualitative analysis.} According to the electromagnetic propagation characteristics of small-sized antenna, the radiation field of UHF RFID tag's antenna is divided into near-field induction and far-field radiation.
%
Suppose that the distance from the observation point to the antenna is $d$, then the boundary of the two fields equals $d^* = \lambda/(2\pi) = c/(2\pi f)$, where $\lambda$, $f$ and $c$ are the wavelength, frequency and speed of the electromagnetic wave. 
%
Since the tag-tag distance $D$ in a LDT is about 1 to 2 centimeters, which is smaller than the boundary $d^* \approx$ 5.16 cm for 925 MHz UHF RFID devices, we should model the near-field signals with the electromagnetic induction model \cite{Twins}.

Similar to a previous work Twins \cite{Twins}, we use the structure-aware interference model to provide reasonable explanation to our observation. In consistent with the traditional T-match structure, the dipole antenna is deemed to be equivalent to an electric dipole like a line and a magnetic dipole like a rectangle in the structure-aware model (Fig. \ref{fig:tag_model}).
%
We denote $H_{Ai}$ as the induced magnetic field in Tag $i$'s antenna which is coupled by the reader's RF signals, and denote $H_{Ci}$ as the induced magnetic field in Tag $i$'s chip which is coupled by the reader's RF signals.

Apart from the induced magnetic field coupled by the reader, the coupling effect of the two adjacent tags also needs to be considered. Therefore, the combined magnetic field in Tag 1's chip $H_1$ is an integration of its induced magnetic fields $H_{C1}$ and $H_{L1}$, the magnetic fields $H_{C2}$ and $H_{L2}$ mutually induced by Tag 2. According to the theoretical transmission model of the RFID signal, the power of the backscattered signal from a tag is in direct proportion to the received power in its chip \cite{RFID-principle}. Therefore, we analyze the variation pattern of $H_1$ and $H_2$ to illustrate our previous observation.

To simplify the representation of $H_1$ and $H_2$, we use two parameters coupling-effect factor $\alpha$ and meta-shield factor $\beta$. $\alpha$ stands for the strength of the mutual-coupling interference from another adjacent tag. According to the structure symmetry of the LDT, two mutual coupling-effect should be equal: $\alpha_{1 \rightarrow 2} = \alpha_{2 \rightarrow 1} = \alpha, 0 \leq \alpha \leq 1$. $\beta$ represents the shield effect from the metal to the RF signals. Since Tag 1 is at the LOS path of Tag 2, the signal strength of Tag 2 is attenuated with a factor $\beta, 0 \leq \beta \leq 1$. 

Thus, $H_1$ and $H_2$ can be represented as:

\begin{equation}
\label{e1}
\begin{split}
H_{1} &= H_{C_1} - H_{A_1} + \alpha \beta (H_{C_2} - H_{A_2}) \\
H_{2} &= \beta (H_{C_2} - H_{A_2}) + \alpha (H_{C_1} - H_{A_1})
\end{split}
\end{equation}

The sign in the above equations stands for the direction of the magnetic field, which is also illustrated in Fig. \ref{fig:tag_model}.
%
Since the tag-tag distance is much smaller than the antenna-tag distance, we suppose that $H_{C1} \approx H_{C2}$ and $H_{A1} \approx H_{A2}$. Because $0 \leq \alpha, \beta \leq 1$, we get:

\begin{equation}
\label{e2}
H_{1} - H_{2} \approx (1 + \alpha \beta - \alpha - \beta)(H_{C_1} - H_{A_1}) \geq 0
\end{equation}

which indicates that the backscatter signal strength of Tag 1 is stronger than that of Tag 2, when there is no liquid leakage.

Because the liquid will not only impair the electromagnetic field near its surface, but also punish the signal penetrating it, we bring in another two parameters $p$ and $q$, $0 \leq p, q \leq 1$ to model these absorption effects of the liquid respectively. Thus, Eqn. \ref{e1} can be revised to the following representations when the liquid leakage occurs:

\begin{equation}
\label{e3}
\begin{split}
H_{1} &= p [H_{C_1} - H_{A_1} + \alpha \beta (q H_{C_2} - H_{A_2})] \\
H_{2} &= p [\beta (H_{C_2} - H_{A_2}) + \alpha (H_{C_1} - q H_{A_1})]
\end{split}
\end{equation}

The propagation penalty factor $q$ is applied to the term where its magnetic field lines directly penetrate through the absorbent cotton, while the absorption effect factor $p$ is applied to every electromagnetic fields near the cotton's surface. Thus, we can get the revised representations in Eqn. \ref{e3}.

When the amount of the leaked liquid increases, $p$ and $q$ get smaller. At first, $H_2$ rises when the decrease of $q$ dominates $H_2$'s variation. Then, along with the decrease of $p$, $H_2$ finally gets weaker. In the meanwhile, $I_1$ gets weaker all the time due to the reduction of $p$ and $q$. Thus, the pattern shown in Fig. \ref{fig:water_procedure} takes place.

\begin{comment}

\subsection{Coupling Effect of Adjacent Tags}

Although the paired tag makes the system more robust in practical, it leads to another interesting phenomenon. Due to the non-negligible distance between the adjacent tags $T_1$ and $T_2$, the backscattered signal from one tag is absorbed by the other one and induces the near-field mutual interference called coupling effect \cite{Twins}. We further find this effect can provide robust features for liquid leakage detection.

To begin with, we analyze the coupling effect with a signal propagation model shown in Fig. \ref{fig:propagation}. Suppose the propagation attenuation in one transmission medium with the distance $d$ is represented as a complex number \cite{TagScan}:

\begin{equation}
\label{e1}
	H(d) = e^{-\alpha d} \cdot e^{j \frac{2 \pi d}{\lambda}}
\end{equation}

where $\alpha$ is the amplitude attenuation constant which only depends on the medium, $\lambda$ is the wave length of the radio when traveling through the medium, and $j$ is the imaginary number. 

Due to the non-negligible coupling effect of the two adjacent tags, we can categorize the propagation paths of RFID backscattered communication into \emph{direct path} where the RFID reader transmits the activation power and the query command, \emph{backscatter path} where the tags transmit identification replies to the reader, and \emph{coupling-effect path} where the tags mutually absorb the backscattered signals from other adjacent tags. 

We denote $S_{T_1}$ and $S_{T_2}$ as the backscattered signals emitted by $T_1$ and $T_2$ respectively. Then, the backscatter signals are superimposed to $S_A$, the carrier signal from the reader's antenna $A$, which describes the mutual interferences of the coupling effect. Thus, $S_{T_1}$ and $S_{T_2}$ are represented as the following two equations:

\begin{equation}
\label{e2}
\begin{split}
	S_{T_1} &= [S_A H(L) + S_{T_2} H(D)] \cdot \Gamma \\
	S_{T_2} &= [S_A H(L) H(D) + S_{T_1} H(D)] \cdot \Gamma
\end{split}
\end{equation}


\begin{figure}[tb]
	\centering
	\subfigure[Theoretical Model]{\includegraphics[width=0.24\textwidth]{img/coupling_model.eps}}
	\subfigure[Experimental Data]{\includegraphics[width=0.24\textwidth]{img/coupling_exp.eps}}
	\caption{\textbf{Experiment results of coupling effect.}}
	\label{fig:coupling}
\end{figure}


where we denote the ratio of the power transmitted by a tag to the power it receives as the equivalent power reflection factor $\Gamma = \tau e^{j\phi}, 0 \leq \tau=Re(\Gamma) \leq 1, 0 \leq \phi=Im(\Gamma) \leq 2 \pi $. $\Gamma$ includes not only the power loss cause by the impedance mismatch but also the shift caused by the antenna gain and the hardware imperfection \cite{RFID-principle}.
%, we define $\Gamma_1$ and $\Gamma_1$ as the reader-tag and tag-tag power reflection factors respectively.

By solving the above equations, we get the transmitted backscattered signals $S_{T_1}$ and $S_{T_2}$:

\begin{equation}
\label{e3}
\begin{split}
S_{T_1} &= \frac{S_A H(L)}{1 + \Gamma^2 H^2(D)} \cdot \Gamma (1 + \Gamma H^2(D))\\
S_{T_2} &= \frac{S_A H(L)}{1 + \Gamma^2 H^2(D)} \cdot \Gamma (1 + \Gamma) H(D)\\
\end{split}
\end{equation}

Finally, we get $S_{A \leftarrow T_1}$ and $S_{A \leftarrow T_2}$, the received backscattered signals at $A$:

\begin{equation}
\label{e4}
\begin{split}
S_{A \leftarrow T_1} = S_{T_1} H(L) &= \frac{S_A H^2(L)}{1 + \Gamma^2 H^2(D)} \cdot \Gamma (1 + \Gamma H^2(D))\\
S_{A \leftarrow T_2} = S_{T_2} H(D) H(L) &= \frac{S_A H^2(L)}{1 + \Gamma^2 H^2(D)} \cdot \Gamma (1 + \Gamma) H^2(D)\\
\end{split}
\end{equation}

We empirically select the model parameters and simulate the attenuation factor $\rho_i = S_{A \leftarrow T_i} / S_A, i \in [1, 2]$ by enumerating the tag distance $D$. Fig. \ref{fig:coupling}(a) shows the simulated amplitudes and phases of the theoretical model. First, the coupling effect degrades both signals when the two tags are close to each other. When $D$ increases, both $\rho_1$ and $\rho_2$ increase due to the attenuation of coupling effect. Then, when $D$ gets larger and larger, the coupling effect periodically gets weaker. And finally $\rho_1$ converges to a stable value and $\rho_2$ fades away to zero.

To verify the model, we conduct several experiments to measure the coupling effect between $T_1$ and $T_2$, by fixing $T_1$ and moving $T_2$ with various tag distance $D$. The results of RSSI and phase readings shown in Fig. \ref{fig:coupling}(b) match the tendency of our theoretical model, despite some experimental errors.

According to Fig. \ref{fig:coupling},  both the theoretical model and experimental data suggest that the coupling effect of adjacent tags can not be neglected if the tag-tag distance $D$ is small. In TagLeak, we elaborately select a fixed tag-tag distance $D = 1.4 cm$ (Section \ref{sec:design}). Thus, all the analyses should be based on the signal models in Equation \ref{e4}.

\begin{figure}[tb]
	\centering
	\includegraphics[width=0.5\textwidth]{img/water.eps}
	\caption{\textbf{Stable RSSI and phase changes before and after liquid leakage.}}
	\label{fig:water}
\end{figure}

\end{comment}

\begin{figure}[tb]
	\centering
	\includegraphics[width=0.5\textwidth]{img/gap_cdf.eps}
	\caption{\textbf{The CDFs of RSSI and phase gaps before and after liquid leakage.}}
	\label{fig:cdf}
\end{figure}

\subsection{Opportunity Behind and Its Stability}

According to the qualitative analysis above and our preliminary experiments, we find the gap between Tag 1's and Tag 2's RSSI sequences and the tendency of each RSSI sequence are of great use to discriminate the leakage states from the normal states. 

To evaluate the stabilities of these features, we conduct numerous experiments with different tags and antenna-tag distances in various environment and across various time. The cumulative distribution function (CDF) of RSSI gap and phase gap are shown in Fig. \ref{fig:cdf}. We can see that the RSSI gap shows great difference between the stages before and after the liquid leakage, while the phase gap does not show such an opportunity. The majority of RSSI gap after the liquid leakage is around zero, which means the RSSI gap between will disappear as long as the volume of the leaked liquid gets larger enough. Moreover, the figure suggests that after the liquid leakage, Tag 2's signal will be better than Tag 1's in most cases.

\section{TagLeak Design}
\label{sec:design}

In this section, we first present a functional overview of TagLeak, followed by details of the technical building blocks.


\subsection{Overview}
 
 Fig. \ref{fig:system} presents the system overview of TagLeak which consists of four main modules: hardware, data acquisition, data analysis and client.
 
 \begin{itemize}
 	\item \textbf{Hardware}: The hardware of TagLeak consists of multiple COTS RFID readers and multiple LDTs. Each reader covers several LDTs around it. The LDTs are categorized into LDT-W for water leakage detection and LDT-L for lubricant leakage detection.

 	\item \textbf{Data Acquisition}: Data acquisition module shields the underlying hardware details, e.g. hardware failure detection and reboot, LDT mapping and data encapsulation, and provides services such as streamed data query, historical alarm query, system configuration and etc. 
 
 	\item \textbf{Data Analysis}: Data analysis module is the core of TagLeak. In this module, the RSSI and phase readings are first pre-processed remove the noises and the interferences. Then, TagLeak extracts features from the processed data and feeds them into a mixed Gaussian-HMM model for the leakage stage estimation and leakage detection. Data acquisition module and data analysis module constitute the TagLeak server. Data acquisition module and data analysis module constitute the TagLeak server.
 	\item \textbf{Client}: The TagLeak Client visualizes the liquid leakage in a digital twin system. As shown in Fig. \ref{fig:deployment}, the vivid 3-dimension display helps the quick localization of the leakage point. Moreover, it provides the administration service for device and LDT management.
 \end{itemize}

\begin{figure}[tb]
	\centering
	\includegraphics[width=0.45\textwidth]{img/system.pdf}
	\caption{\textbf{TagLeak system overview.}}
	\label{fig:system}
\end{figure}
 
Next, we introduced the components of data analysis module in detail.

\subsection{Signal Preprocessing}

Raw RSSI and phase data are intrinsically noisy and need processing to improve the accuracy and robustness of further analysis. Before signal processing, we first segment the received signal with a time window $W$. Length of the window is determined by diffusion rates of the leaked liquid in the absorbent cotton. Consider that different liquid have different diffusion rates in the absorbent cotton which further affects the change rate of the RF signal, $W$ is empirically selected according to the liquid material.
After segmentation, the data segments will be pre-processed through the following three steps:

\textbf{Noise Filtering.} The received RSSI and phase signal are susceptible to the variation of the tag which is caused by mechanical vibration. Fortunately, we find that signal’s variation frequency caused by tags’ vibration is much higher than that caused by liquid leakage. Therefore, the noise can be filtered out with a Butterworth low-pass filter.

\textbf{State Estimation.}
For further data analysis and leakage identification, we first classify the data segment into three states: \emph{stable}, \emph{unstable} and \emph{unreachable}. Segments that belong to different states will be processed with different methods in the subsequent data analysis module. The data is in \emph{stable} state most of the time, but the liquid leakage and the environmental interferences will bring instability to the RF signals, making the signal transfers to the \emph{unstable} state. When the cotton is saturated with the leaked liquid and the undissolved liquid drops adhering to the surfaces of RFID tags, the signal will be absorbed by the liquid, making the signal \emph{unreachable} to the reader.


TagLeak utilizes the RSSI and phase readings for state estimation. Specifically, in the \emph{unreachable} state, the number of samples that the reader received per unit time will increase significantly. Therefore, the \emph{unreachable} state can be identified using a threshold for the number of samples in a segment. Segments that belong to the \emph{stable} and \emph{unstable} states can be distinguished based on the RSSI variation of the samples in the segment with an empirical threshold. We use a Gaussian mixture model (GMM) to identify the \emph{stable} state, since the average value $\mu$ can change over time due to some unpredictable but permanent environmental variations. If a data segment is identified as \emph{stable}, it will be fed into the GMM model for parameter update. Then, $\mu$ produced by the GMM model is further leveraged for data normalization.

\textbf{Interference Determination.}

As mentioned earlier, the unstability of the data (i.e., the \emph{unstable} state) is caused by liquid leakage or multipath interference. In TagLeak, we would like to filter out the \emph{unstable} segments that caused by multipath interferences. Traditional methods which leverage channel hopping to remove the multipath interference offline is obviously inapplicable for leakage detection \cite{OmniTrack}. In TagLeak, we leverage the design of {\em dual}-tag detector (i.e., the LDT) for robust online multipath determination. 

As discussed earlier, since the tag-tag distance is small (about 1.4cm in our case), the variation of the signal that caused by the multipath is similar for both the two tags. To verify this assumption, we conduct a set of experiments to observe how different interference affect the signal from the two tags. Fig \ref{fig:interference} shows the experimental result. We can see that multipath interferences will always result in a consistent trend for Tag 1 and Tag 2. However, if the variation is caused by liquid leakage, the signals from the two tags will exhibit different variation trend, as shown in Fig \ref{fig:water_procedure}. The reason behind is discussed in Section \ref{sec:model}.

Based on the above result, we propose to identify the interfered segments based on the similarity between the signal from Tag 1 and Tag 2. Specifically, we measure the similarities of the RSSI readings and phase readings from the two tags by calculating their Pearson correlation coefficients, respectively. Features, such as kurtosis, spectrum characteristics, variation trend are also considered and fed into a classic CART decision tree. If a segment is deemed as an interference by our classifier, we filter it out first.

\begin{figure}[tb]
	\centering
	\includegraphics[width=0.5\textwidth]{img/water_procedure.pdf}
	\caption{\textbf{Signal variations during the process of liquid leakage.}}
	\label{fig:water_procedure2}
\end{figure}

\subsection{Feature Extraction}
\label{sec:design:preprocessing}

In this subsection, we discuss how to extract features from the processed signals for liquid leakage detection. Before this, we first introduce the variation trend of the signals from the two tags (as shown in Fig. \ref{fig:water_procedure2}) when leakage occurs, which acts as a key feature of liquid leakage. As shown in the figure, the liquid leakage process will go through three stages: pre-leaking stage (termed as $G_{pre}$), leaking stage (termed as $G_{ing}$), and post-leaking stage (termed as $G_{post}$). We describe the signal patterns of each stage in Section \ref{sec:model}.


As the previous description shows, the special variation trend of the signal can be considered as an important indicator about whether liquid leakage occurs. Based on this observation, we propose to consider the similarity between the collected signal (including RSSI and phase) sequence and the pre-constructed signal profile as a feature for leakage detection. Since the leakage speed may be different cross different cases, we propose using Dynamic Time Warping (DTW) to align the collected signal sequence to the signal profile. 


However, a problem we meet here is how to accurately capture the $G_{ing}$ stage from the collected signal. We solve this problem leveraging the fact that signal in the $G_{ing}$ stage exhibits much higher variation than that in the $G_{pre}$ and $G_{post}$ stages. Therefore, we detect the starting and ending points of the $G_{ing}$ stage using a moving window (the window here is different from that mentioned in Section \ref{sec:design:preprocessing}). Specifically, in each window, we calculate the variation of the phase samples in the window (the reason we use the phase samples is that they are more sensitive to the liquid leakage, as shown in Fig. \ref{fig:water_procedure2}). 
%
%{\color{red}Fig XX} shows the signal variation calculated over successive windows, based on the phase samples shown in Fig \ref{fig:water_procedure}. 
%
Clearly, the stating and ending points can be identified as the time points where the variation increases and decreases significantly. Then we can capture the $G_{ing}$ stage.

Apart from the variation trend of the signal discussed above, some statistical features like the quantity of readings, the variance sequence of multiple sub-segments and the average distance between the segment, and the stable value µ, are also extracted for leakage detection. TagLeak extract all these $M$ features from each signal segment. The extracted features form a $M \times K$ feature vector which is fed to the subsequent leakage detection module for further analysis.
%{\color{red}(as shown in Table XX)} 
 
\subsection{Leakage Detection Algorithm}

As discussed in the previous section, variation trend of the signal acts as a key indicator of whether liquid leakage occurs. That is to say, instead of considering the features of each signal segment independently, we should make a joint consideration of a sequent of signal segments for leakage detection. Therefore, our leakage detection problem is translated to a classification problem that: given a sequence of signal segments and the features for each segment, what is the probability that liquid leakage occurred? 

We propose to exploit Markov-based classifier to tackle this problem. In TagLeak, two Gaussian HMM models (denoted by $\mathcal{M}_1$ and $\mathcal{M}_2$) are trained to estimate the probabilities of normal and abnormal events, respectively. Here the normal event means that leakage has not occurred and the abnormal event means that leakage has occurred. $\mathcal{M}_1$ involves two states: $G_{pre}$ and $G_{und}$, where $G_{und}$ is an undetermined stage, which is possible to transfer to all the other states. $\mathcal{M}_2$ involves all the four states.

We train $\mathcal{M}_1$ and $\mathcal{M}_2$ exploiting data sets collected from normal event and abnormal event, respectively. Specifically, all the samples in these two datasets are simultaneously fed into $\mathcal{M}_1$ and $\mathcal{M}_2$ to generate a compound representation. For each segment, each HMM model outputs a decoded hidden state sequence of this segment and the logarithmic probability that it belonging to this model (For example, the probability that a segment belongs to $\mathcal{M}_1$ means the probability that this segment is collected when leakage has not occurred). Then, we concatenate the two representations into a vector, and further fed it to a SVM (Support Vector Machine) based classifier, which is trained to determine whether the leakage occurs based on the output vector of the HMM model.

\begin{comment}
\begin{figure}[tb]
	\centering
	\includegraphics[width=0.48\textwidth]{img/exp/prototype.jpg}
	
	\caption{\textbf{The TPR and FPR over different volumes of different liquid categories.}}
	\label{exp:prototype}
\end{figure}
\end{comment}

\section{Evaluation}
\subsection{Implementation}
We implement a prototype of TagLeak with COTS ImpinJ R420 RFID reader and our LDT made of a pair of Alien AZ-9640 tags and a piece of absorbent cotton, 10 cm long by 2 cm wide by 1.4 cm by thick.%, as shown in Fig. xx. 

As we mentioned before, to support the management of multiple readers serving multiple LDTs, we decouple the device management module, the data acquisition module and the data analysis module. 
%
The first two modules are implemented with ImpinJ's application programming interfaces in Java, and are packed into a Websocket socket server to support multiple clients. The encapsulation with Websocket is very suitable for querying streamed data because it eliminates the overhead of constructing multiple HTTP connections while providing more friendly interfaces than transport-layer protocols e.g. TCP.
%
The data analysis module is first implemented in Matlab for a better understanding of the sampled data and a more convenient model exploration with its simulation tools. Then, in the real-world deployment of TagLeak in Pavatar, we optimize the algorithms and pack them into a pure-Java project.


\subsection{Methodology}

Since TagLeak focus on the binary classification problem of liquid leakage detection, we mainly use two metrics to evaluate the performance of the proposed approach: true positive rate (TPR) and false positive rate (FPR).
%
TPR represents the percentage that TagLeak correctly detects the liquid leakage. 
%
FPR represents the percentage that interferences are mistaken for the liquid leakage by TagLeak.

We mainly discuss the impacts of the following settings on TagLeak's performance:

\begin{itemize}
	
	\item \textbf{Liquid-related parameter 1 - Volume} ($V$). The volume of the leaked liquid $V$ reflects the sensitivity of the detection approaches. To evaluate TagLeak, we vary $V$ by 10 mL each time while keeping the duration of the leakage process unchanged.
	
	\item \textbf{Liquid-related parameter 2 - Category} ($C$).
	%
	Since TagLeak is mainly targeted for non-intrusive liquid leakage detection for the water-cooling machines and the lubricant-recycling machines, we evaluate its performances with two categories of liquid: water and lubricant. This experiment evaluates the generalizability of TagLeak.
	
	\item \textbf{Environment-related parameter 1 - Reader-Tag Distance} ($L$). We keep using the notion of $L$ to represent the distance between the RFID reader's antenna and LDT(s). To a great extent, $L$ determines the ease of deployment and the coverage of one single antenna. To evaluate this parameter, we move a LDT by 50 cm each time.

	\item \textbf{Environment-related parameter 2 - Multipath and Interference}. These two settings mainly evaluate the practicability of TagLeak system. We choose two different environments with different amounts of multipath. Moreover, we evaluate TagLeak under different kinds of interferences varying in time, location, materials and etc.

	\item \textbf{Algorithm-related parameter 1 - Window Size} ($K$). $K$ stands for the number of segments in one data window, which is the basic time unit to conduct a prediction in TagLeak. By selecting $K$, we trade off between the accuracy and the prediction time.
	
	%The larger the value of K is, the more information is processed each time, at the same time, the detection time will be longer. We observe the accuracy of the prediction by adjusting the value of K in our model.

	\item \textbf{Algorithm-related parameter 2 - Classifier}. With the same extracted features, we evaluate different classification methods, e.g. whether they capture the temporal correlation of the segments in one data window.
	
\end{itemize}

%\subsection{Impact of model design} 

\begin{figure}[tb] 
	\centering
	\subfigure[Water Leakage Detection]{\includegraphics[width=0.24\textwidth]{img/exp/exp-volume-water.eps}}
	\subfigure[Lubricant Leakage Detection]{\includegraphics[width=0.24\textwidth]{img/exp/exp-volume-lubricant.eps}}
	\caption{\textbf{The TPR and FPR over different volumes of different liquid categories.}}
	\label{exp:volume}
\end{figure}

\begin{figure}[tb]
	\centering
	\subfigure[Detection with little multipath]{\includegraphics[width=0.24\textwidth]{img/exp/exp-distance-normal.eps}}
	\subfigure[Detection with rich multipath]{\includegraphics[width=0.24\textwidth]{img/exp/exp-distance-multipath.eps}}
	\caption{\textbf{The TPR and FPR over different Reader-Tag distances in different environments.}}
	\label{exp:distance}
\end{figure}

\subsection{Impact of Liquid Volume} 

The sensitivity of the detection approaches can be inferred from the relationship between the detection accuracy and the liquid volume $V$. An approach that achieves higher accuracy over less $V$ is preferred.

We control $V$ by selecting the leaking speed of the medical infusion equipment and remaining a constant duration. The reader-tag distance $L$ is kept 50 cm unchanged. {\color{red} Fig. \ref{exp:volume}(a) shows the results of water leakage detection over different $V$s}. First, TagLeak can achieve 60\% when $V$ is only 5 ml. Then, with the increase of volume, its TPR can reach almost 99\%. The TPR over a small quantity of water is acceptable ($>$ 83\%) although the pattern of signal variation is less evident.{\color{red} Last, the FPR of TagLeak is smaller than 1\% over all different $V$s}. We own this result to the design of LDT, which provides a robust and distinctive pattern while preserves the detection from external interferences.

\subsection{Impact of Liquid Category}

The generalizability of the detection approaches can be evaluated by applying them to different categories of liquid. Water and lubricant, as the most common as well as critical liquid in modern factories, are taken into consideration.

The same as the settings in the last experiment, we further measures the relationship between the detection accuracy of lubricant and its volume. 
%degradation
We can compare the results in Fig. \ref{exp:volume} (a) and (b). First of all, the trends are similar: a larger amount of the leaked liquid lead to more distinctive patterns. 
%
However, the TPR of lubricant detection ($>$ 80\%) is much smaller than that of water detection, and TagLeak fails to provide reliable predictions when $V$ is relatively small. 
%
To illustrate the performance degradation, we conclude the two following reasons: i) lubricant contains less polar molecules and have significantly less absorption of electromagnetic waves than water \cite{Material2}; ii) the diffusion speed and area of lubricant is much smaller due to its higher viscosity, which further diminishes signal variations.

Thus, to enhance TagLeak with robust lubricant detection, not only the detection algorithm and the quality of the training dataset, but also the design of LDT-L should be improved.

\begin{figure}[tb]
	\centering
	\subfigure[Detection with little interferences]{\includegraphics[width=0.24\textwidth]{img/exp/exp-distance-multipath-int-little.eps}}
	\subfigure[Detection with heavy interferences]{\includegraphics[width=0.24\textwidth]{img/exp/exp-distance-multipath-int.eps}}
	\caption{\textbf{The TPR and FPR over different Reader-Tag distances under different interferences.}}
	\label{exp:interference}
\end{figure}

\begin{figure}[tb]
	\centering
	\subfigure[Interfered by metal]{\includegraphics[width=0.24\textwidth]{img/exp/int-metal.eps}}
	\subfigure[Interfered by human body]{\includegraphics[width=0.24\textwidth]{img/exp/int-body.eps}}
	\caption{\textbf{The TPR and FPR over different interference duration under different material.}}
	\label{exp:distinguish}
\end{figure}

\subsection{Impact of Reader-Tag Distance}

One of the advantages of TagLeak is non-intrusive characteristic, because the remote wireless sensing does nearly not block the limited operating space around the auxiliary machines. Thus, the detection range or the reader-tag distance $L$ is a critical metric.

We control $L$ by moving the LDT-W away from the reader's antenna by 50 cm each time, and keep the liquid volume 30 mL. 
%
Fig. \ref{exp:distance} (a) shows the results of water leakage detection over different $L$s. 
%
The trend of Tag's performance is consistent with our preliminary expectations: As $L$ gets larger, the performance slightly degrades. {\color{red}With the TPR over 99\%, TagLeak can provide robust water leakage detection when $L$ is no more than 1 meter}. 

 
\subsection{Impact of the Amount of Multipath}

All of the above basic experiments are conducted in a laboratory with little multipath. However, TagLeak is designed for real-world industrial Internet applications, we evaluates its practicability by deploying the prototype in a rich-multipath environment with many metal tubes.

As shown in Fig. \ref{exp:distance} (b), although the performance of TagLeak in the rich-multipath environment is a little poorer, {\color{red}but the average TPR of 91.3\% is acceptable in practice}.
%
Actually, the degradation is very small, because in a complicated but stable environment, the signal variations are mainly caused by the process of the liquid leakage under LDT's coupling effect.

\begin{figure}[tb]
	\centering
	\subfigure[{Window size and performance}]{\includegraphics[width=0.4\textwidth]{img/exp/K.eps}}
	\subfigure[Classifier and performance]{\includegraphics[width=0.4\textwidth]{img/exp/exp-model-classifier.eps}}
	\caption{\textbf{The TPR and FPR over different settings of different model parameters.}}
	\label{exp:model}
\end{figure}



\subsection{Impact of Interferences}

Interferences occurring in the LOS path between the reader's antenna and the LDT can cause significant fluctuations in the RF signals. Fortunately, LDT produces two different patterns of the signal variation when there exists the interference and the leakage, which can be distinguished by our detection algorithm.

To evaluate the performance of TagLeak, we simulate various levels of interferences by changing the position and duration of the different obstacles.
%
{\color{red}Fig. \ref{exp:interference} (a) shows that TagLeak keeps a high TPR ($>$ 90\%) along with a low FPR ($<$ 10\%), which means TagLeak can effectively tolerate the interferences. With the increase of interferences (Fig. \ref{exp:interference} (b)), the TPR of TagLeak remains almost unchanged, but the FPR goes up slightly.}

\begin{figure}[tb]
	\centering
	\includegraphics[width=0.5\textwidth]{img/deployment.pdf}
	\caption{\textbf{Real-world deployment and visualization of TagLeak in Pavatar.}}
	\label{fig:deployment}
\end{figure}


Then, we evaluate the performance of our interference determination algorithm. A metal plate and one hand of an author are used as the obstacles blocking the LOS path of the LDT. 
%
Fig. \ref{exp:distinguish} (a) and (b) shows the results respectively. The TPR means the accuracy that the interferences are recognized correctly and the FPR stands for the probability that the sequences during the liquid leakage are falsely deemed as interferences.
%
{\color{red}The results suggest that TagLeak can effectively recognize the interferences induced by the moving metal. However the interferences caused by the human body is difficult for recognition, due to the uncertainty of the body movement. Although the FPR of the interference determination algorithm is relatively high, the interferences induced by the human body can also be effectively distinguished by the detection algorithm, as shown in Fig. \ref{exp:interference}.
}
\subsection{Impact of Model Parameters}

The design of the detection algorithm can also significantly affect the performance. Here, we mainly focus on two key components: the detection window size $K$ and the backend classifier.

Window size $K$ determines the length of time we take to collect data, a smaller $K$ stands for a higher level of sensitivity for the leakage detection. Fig. \ref{exp:model} (a) shows that $K$ has relatively small effect on the accuracy of TagLeak. However, with the increase of $K$, the processing delay is reduced, because less data windows are generated. To balance the sensitivity and the system overhead, we set $K$ equals to 5 in the other experiments.

Fig. \ref{exp:model} (b) shows the comparison of two backend classifier. The HMM+SVM approach is TagLeak's current choice and the pure SVM are fed in with whole features without any representation conversion. We evaluate the two models with the whole test dataset consisting of multiple samples collected in various scenarios. From the figure, we know that both of the models achieve relatively high TPR, 90.1\% and 88.9\% respectively. But TagLeak's approach achieves lower FPR at about 4.9\%.
%
We own this improvement to the import of HMM. Because it considers more about the temporal correlation between adjacent segments, which can tell from the differences between a leakage sequence and an interference sequence that the fluctuations in the latter one will disappear and the signal will come back to $G_{pre}$.

\section{Conclusion and Future Works}

In the past few years, RFID technology has shown its capability of providing non-intrusive and battery-free sensing in modern cyber-physical systems. We advance the state of the arts by applying it to a widespread problem, liquid leakage detection in smart factories and smart plants.
%
This problem had not been well-solved before because .
%
Our proposal called TagLeak first extracts robust features from the signal model based on the coupling effect of two adjacent tags, and then localizes the liquid leakage from pre-processed data stream with a HMM-based detection algorithm.
%
The solution of TagLeak is not only non-intrusive and low-cost in deployment, but also real-time and robust in effectiveness and efficiency.
%
The experimental results tell that TagLeak achieves a higher than 90.2\% average accuracy while keeps the recall below 14.3\%.

As an exploration of the industrial Internet, we deploy a prototype of TagLeak in a real-world digital twin system Pavatar for the next-generation surveillance of UHVCSs. In Pavatar, we design two types of LDTs to detect water leakage and lubricant leakage respectively  \cite{Pavatar}\cite{Pavatar-paper}. 
%
As shown in Fig. \ref{fig:deployment}, we use some movable brackets to fix the RFID readers and antennas on the auxiliary machines, and belts LDTs around their flanges. There are together 38 potential leakage points monitored in a factory building of 60 meter long by 25 meter wide in a UHVCS. 
%
The application of liquid leakage detection has been running for 3 months until the due date of this paper. 

Although TagLeak has stepped forward to the Internet-of-Everything in industrial scenarios, there still have many future works waiting to be explored. 
%
First, the volume of the leaked liquid can be inferred if the relationship between it and the patterns of RFID signals is precisely modeled. 
%
Second, the antenna-tag distance in TagLeak is limited to around 1-2 meters for a better quality of received signals. For a wider coverage, the length of $L$ should be extended, but the rich multipaths induced by machines and their complicated pipelines should be modeled.
%
Third, a COTS reader has a strict limitation on the inquiry frequency, e.g. 40Hz for ImpinJ R420. If a reader is supposed to serve more LDTs, the average quantity of the readings from one LDT decreases and thus leakage detection should be conducted with incomplete information.
%
Moreover, liquid leakage detection is also in desperate need in other buildings in UHVCSs, e.g. valve halls. However, the strong electromagnetic environment in valve halls bring severe challenges for the backscatter communication.
%
In the future, we plan to solve the challenges above and further enhance the capabilities of TagLeak.

\bibliographystyle{unsrt}
\bibliography{ref}

\end{document}
